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In the opening chapters of this volume, we outlined a series of challenges facing psychiatry, as well as a description of its various promises, and suggested that taking a computational perspective could potentially illuminate a way forward. In this concluding chapter, we revisit these challenges and promises, in the context of what transpired at this Ernst Strüngmann Forum, to highlight the connections between the various themes raised. In particular, we will bring out the points of agreement and disagreement between the discussion groups and the chapters that arose from those discussions. We conclude with a description of the efforts, current and ongoing, to bring the potential synergy between psychiatry and computational neuroscience emphasized in this volume to a reality in the scientific and clinical arenas.

Psychiatry faces a number of challenges due largely to the complexity of the relationship between mind and brain. Starting from the now well-justified assumption that the mind is instantiated in the physical substrate of the brain, understanding this relationship is going to be critical to any understanding of function and dysfunction. Key to that translation from physical substrate to mental function and dysfunction is the computational perspective: it provides a way of translating knowledge and understanding between levels of analysis (Churchland and Sejnowski 1994). Importantly, the computational perspective enables translation to both identify emergent properties (e.g., how a molecular change in a receptor affects behavior) and consequential properties (e.g., how an external sociological trauma can lead to circuit changes in neural processing). Given that psychiatry is about treating harmful dysfunction interacting across many levels (from subcellular to sociological), this chapter argues that the computational perspective is fundamental to understanding the relationship between mind and brain, and thus offers a new perspective on psychiatry.

This chapter proposes a new framework for diagnostic nosology based on Bayesian principles. This novel integrative framework builds upon and improves the current diagnostic system in psychiatry. Instead of starting from the assumption that a diagnosis describes a specific unitary dysfunction that causes a set of symptoms, it is assumed that the underlying disease causes the clinician to make a diagnosis. Thus, unlike the current diagnostic system, this framework treats both symptoms and diagnostic classification as consequences of the underlying pathophysiology. Comorbidities are therefore easily incorporated into the framework and inform, rather than hinder, the diagnostic process. Further, the proposed framework provides a bridge that links putative constructs related to pathophysiology and clinical diagnoses related to signs and symptoms. Crucially, this novel framework explicitly provides an iterative approach, updating and selecting the best model, based on the highest-quality available evidence at any point. It can account for and incorporate the longitudinal course of an illness. This chapter details its theoretical basis and provides clinical examples to illustrate its utility and application. It is hoped that the framework will enhance our understanding of individual differences in brain function and behavior and ultimately improve treatment outcomes in psychiatry.

Psychiatry faces numerous challenges: the reconceptualization of symptoms and diagnoses, disease prevention, treatment development and monitoring of its effects, and the provision of individualized, precision medicine. To confront the complexity and heterogeneity intrinsic to brain disorders, psychiatry needs better biological, quantitative, and theoretical grounding. This chapter seeks to identify the sources of complexity and heterogeneity, which include the interplay between genetic and epigenetic factors with the environment and their impact on neural circuits. Computational approaches provide a framework to address complexity and heterogeneity, which cannot be seen as noise to be eliminated from diagnosis and treatment of disorders. Complexity and heterogeneity arise from intrinsic features of brain function, and thus present opportunities for computational models to provide a more accurate biological foundation for diagnosis and treatment of psychiatric disorders. Challenges to be addressed by a computational framework include: (a) improving the search for risk factors and biomarkers, which can be used toward primary prevention of disease; (b) representing the biological ground truth of psychiatric disorders, which will improve the accuracy of diagnostic categories, assist in discovering new treatments, and aid in precision medicine; (c) representing how risk factors, biomarkers, and the underlying biology change through the course of development, disease progression, and treatment process.

Scientists and clinicians can utilize a model-based framework to develop computational approaches to psychiatric practice and bring scientific discoveries to a clinical interface. This chapter describes a general modeling perspective, which complements those derived in previous chapters, and provides distinct examples to highlight the scientific and preclinical research that can evolve out of a computational framework to offer new tools for clinical practice. It begins by reviewing areas of theoretical and modeling studies that have reached a critical mass and outlines the pathophysiological insights that have been revealed. The phasic dopamine temporal difference model shows how neurophysiological and neuroanatomical research, incorporated into a learning circuit model, provides a constrained hypothesis testing framework, related to the likely multiple mechanisms contributing to addiction. A potential application of generative models of neuroimaging measurements (dynamic causal models of EEG data) is described to predict individual treatment responses in patients with schizophrenia. The third example offers a novel approach to quantifying patient outcomes under a “recovery model” of psychiatric illness. In conclusion, consideration is given to the community efforts needed to support the validation of these and future applications.

This chapter provides an illustrative treatment of psychiatric morbidity that offers an alternative to the standard nosological model in psychiatry. It considers what would happen if we treated diagnostic categories not as putative causes of signs and symptoms, but as diagnostic consequences of psychopathology and pathophysiology. This reconstitution (of the standard model) opens the door to a more natural formulation of how patients present and their likely response to therapeutic interventions. The chapter describes a model that generates symptoms, signs, and diagnostic outcomes from latent psychopathological states. In turn, psychopathology is caused by pathophysiological processes that are perturbed by (etiological) causes (e.g., predisposing factors, life events, therapeutic interventions). The key advantages of this nosological formulation include: (a) the formal integration of diagnostic categories and latent psychopathological constructs; (b) the provision of a hypothesis or model space that accommodates formal evidence-based hypothesis testing or model selection; (c) the ability to predict therapeutic responses; and (d) a framework that allows one to test hypotheses about the interactions between pharmacological and psychotherapeutic interventions. This chapter shows what might be possible, through the use of idealized simulations. These simulations can be regarded as a (conceptual) prospectus that motivates a computational nosology for psychiatry.

Modern psychiatry seeks to treat disorders of the brain, the most complex and least understood organ in the human body. This complexity poses a set of challenges that make progress in psychiatric research particularly difficult, despite the development of several promising novel avenues of research. New tools that explore the neural basis of behavior have accelerated the discovery in neuroscience, yet discovery into better psychiatric treatments has not kept pace. This chapter focuses on this disconnect between the challenges and promises of psychiatric neuroscience. It highlights the need for diagnostic nosology, biomarkers, and better treatments in psychiatry, and discusses three promising conceptual advances in psychiatric neuroscience. It holds that rigorous theory is needed to address the challenges faced by psychiatrists.

This chapter provides specific research examples on the neurobiology of mental illness—using psychosis as a case in point—that may begin to rise to the level of “facts,” or at least “almost facts” or strong “hints,” about important etiological mechanisms that need to be explained to capture key components of at least some facets of mental illness. These examples are then used to illustrate where computational psychiatry approaches may help. In particular, there is an opportunity to provide links across different levels of analysis (e.g., behavior, systems level, specific circuits and even genetic influences) in ways that can lead to a more unified framework for understanding the apparent multitude of impairments present in psychosis, which may in turn lead to the identification of new treatment or even prevention targets. This chapter also discusses some of the known conundrums about the etiology of mental illness that need to be accounted for in computational frameworks, including the presence of heterogeneity within current diagnostic categories, the vast degree of comorbidity across current diagnostic categories, and the need to reconceptualize the dimensionality versus categorical nature of mental illness.

Vast spectra of biological and psychological processes are potentially involved in the mechanisms of psychiatric illness. Computational neuroscience brings a diverse toolkit to bear on understanding these processes. This chapter begins by organizing the many ways in which computational neuroscience may provide insight to the mechanisms of psychiatric illness. It then contextualizes the quest for deep mechanistic understanding through the perspective that even partial or nonmechanistic understanding can be applied productively. Finally, it questions the standards by which these approaches should be evaluated. If computational psychiatry hopes to go beyond traditional psychiatry, it cannot be judged solely on the basis of how closely it reproduces the diagnoses and prognoses of traditional psychiatry, but must also be judged against more fundamental measures such as patient outcomes.

Advances in our understanding of brain function and dysfunction require the integration of heterogeneous sources of data across multiple levels of analysis, from biophysics to cognition and back. This chapter reviews the utility of computational neuroscience approaches across these levels and how they have advanced our understanding of multiple constructs relevant for mental illness, including working memory, reward-based decision making, model-free and model-based reinforcement learning, exploration versus exploitation, Pavlovian contributions to motivated behavior, inhibitory control, and social interactions. The computational framework formalizes these processes, providing quantitative and falsifiable predictions. It also affords a characterization of mental illnesses not in terms of overall deficit but rather in terms of aberrations in managing fundamental trade-offs inherent within healthy cognitive processing.

Psychiatry has found it difficult to develop a nosology that allows for the targeted treatment of disorders of the mind. This article sets out a possible way forward: harnessing systems theory to provide the conceptual constraints needed to link clinical phenomena with neurobiology. This approach builds on the insight that the mind is a system which, to regulate its environment, needs to have a model of that environment and needs to update predictions about it using the rules of inductive logic. It can be shown that Bayesian inference can be reduced to updating beliefs based on precision-weighted prediction errors, where a prediction error is the difference between actual and predicted input, and precision is the confidence associated with the input prediction. Precision weighting of prediction errors entails that a given discrepancy between outcome and prediction means more, and leads to greater belief updates, the more confidently the prediction was made. This provides a conceptual framework linking clinical experience with the pathophysiology underlying disorders of the mind. Limitations of this approach are discussed and ways to work around them illustrated. Initial steps and possible future directions toward a nosology based on failures of precision weighting are discussed.

Psychiatric classifications categorize how patients present to mental health care professionals and are necessarily utilitarian. From the clinician’s perspective, the most important goal of a psychiatric classification is to assist them in managing their patients’ psychiatric conditions by facilitating the selection of effective interventions and predicting management needs and outcomes. Due to the field’s lack of understanding of the neurobiological mechanisms underlying the psychiatric disorders in both the Diagnostic and Statistical Manual of Mental Disorders (DSM) and the International Classification of Diseases (ICD), diagnosis and treatment are only loosely related, thus limiting clinical utility. Both DSM and the chapter on mental and behavioral disorders in ICD adopted a descriptive atheoretical categorical approach that defines mental disorders according to syndromal patterns of presenting symptoms. This chapter discusses the fundamental challenges that underlie this decision. It then reviews the Research Domain Criteria (RDoC) project, a research framework established by the U.S. National Institute of Mental Health (NIMH) to assist researchers in relating the fundamental domains of behavioral functioning to their underlying neurobiological components. Designed to support the acquisition of knowledge of causal mechanisms underlying mental disorders, RDoC may facilitate a future paradigm shift in the classification of mental disorder.

Computational modeling in psychiatry has generally followed from efforts to understand cognitive processes (McClelland and Rumelhart 1986) or the nervous system (Hodgkin and Huxley 1952). This stands to reason: psychiatric disorders are disorders of thought and central nervous system activity. This chapter argues that the computational science of collapse, which describes the manner and likelihood of failures in complex systems, provides a framework in which to use computational modeling for relating mechanisms to behavioral outcomes. This science, known as reliability engineering, is a branch of applied probability theory that has now been used for almost a century to help understand and predict how inorganic, complex systems break down. The idea of a fault tree analysis is introduced, a tool developed in reliability engineering which may be able to incorporate and provide a broader structure for more traditional computational models. Finally, some of the current challenges of psychiatric classification are unpacked, and discussion follows on how this framework might be adapted to provide a unifying framework for classification and etiology.

The quest to understand the relationship between neural activity and behavior has been ongoing for well over a hundred years. Although research based on the stimulus-and-response approach to behavior, advocated by behaviorists, flourished during the last century, this view does not, by design, account for unobservable variables (e.g., mental states). Putting aside this approach, modern cognitive science, cognitive neuroscience, neuroeconomics, and behavioral economics have sought to explain this connection computationally. One major hurdle lies in the fact that we lack even a simple model of cognitive function. This chapter sketches an application that connects neuromodulator function to decision making and the valuation that underlies it. The nature of this hypothesized connection offers a fruitful platform to understand some of the informational aspects of dopamine function in the brain and how it exposes many different ways of understanding motivated choice.

Biological psychiatry is at an impasse. Despite several decades of intense research, few, if any, biological parameters have contributed to a significant improvement in the life of a psychiatric patient. It is argued that this impasse may be a consequence of an obsessive focus on mechanisms. Alternatively, a risk prediction framework provides a more pragmatic approach, because it aims to develop tests and measures which generate clinically useful information. Computational approaches may have an important role to play here. This chapter presents an example of a risk-prediction framework, which shows that computational approaches provide a significant predictive advantage. Future directions and challenges are highlighted.

The burden of depression is substantially aggravated by relapses and recurrences, and these become more inevitable with every episode of depression. This chapter describes how computational psychiatry can provide a normative framework for emotions and an integrative approach to core cognitive components of depression and relapse. Central to this is the notion that emotions effectively imply a valuation; thus they are amenable to description and dissection by reinforcement-learning methods. It is argued that cognitive accounts of emotion can be viewed in terms of model-based valuation, and that automatic emotional responses relate to model-free valuation and the innate recruitment of fixed behavioral patterns. This model-based view captures phenomena such as helplessness, hopelessness, attributions, and stress sensitization. Considering it in more atomic algorithmic detail opens up the possibility of viewing rumination and emotion regulation in this same normative framework. The problem of treatment selection for relapse and recurrence prevention is outlined and suggestions made on how the computational framework of emotions might help improve this. The chapter closes with a brief overview of what we can hope to gain from computational psychiatry.

Clinical heterogeneity presents important challenges to optimizing psychiatric diagnoses and treatments. Patients clustered within current diagnostic schema vary widely on many features of their illness, including their responses to treatments. As outlined by the American Psychiatric Association Diagnostic and Statistical Manual (DSM), psychiatric diagnoses have been refined since DSM was introduced in 1952. These diagnoses serve as the targets for current treatments and supported the emergence of psychiatric genomics. However, the Research Domain Criteria highlight DSM’s shortcomings, including its limited ability to encompass dimensional features linking patients across diagnoses. This chapter considers elements of the dimensional and categorical features of psychiatric diagnoses, with a particular focus on schizophrenia. It highlights ways that computational neuroscience approaches have shed light on both dimensional and categorical features of the biology of schizophrenia. It also considers opportunities and challenges associated with attempts to reduce clinical heterogeneity through categorical and dimensional approaches to clustering patients. Finally, discussion will consider ways that one might work with both approaches in parallel or sequentially, as well as diagnostic schema that might integrate both perspectives.